Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM

EEG-based emotion classification using spiking neural networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications

Personalised modelling with spiking neural networks integrating temporal and static information.

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual

Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modelling, and understanding of stream data.

This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three bench mark problems. The first one is early prediction of patient sleep stage event from temporal physiological data. The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning techniques or spiking neural network reservoirs with arbitrary mapping of the variables.Comment: Accepted by IEEE TNNL

Working towards improved conceptualisation and identification of gaming disorder and co-occurring addictions in gamers

This doctoral research thesis investigated the neurophysiological underpinnings of gaming disorder (GD), and the way in which co-occurrence can influence and correlate with GD in a clinical and a multi-cultural context. The unique contribution of knowledge was (i) the assessment of the neurophysiological expression of gamers using a novel spiking neural network (SNN) methodology; (ii) exploring co-occurrence in gamers and substance abstinent gamers; and (iii) exploring co-occurrence in gamers across three different individualistic countries (i.e., Australia, New Zealand, and the United Kingdom). The conceptualisation of GD and related methodologies were explored using multiple systematic research methods. A number of methodologies were then employed, including the use of electroencephalographic (EEG) data, a machine learning (ML) approach which utilised a novel SNN architecture (i.e., the NeuCube), and the use of surveys to reach a clinical cohort and three cohorts spanning three different countries in an effort to investigate the way in which co-occurrence may influence gamers and at-risk gamers. The results of the empirical studies indicated that: (i) problematic gamers experience different neurophysiological expression than those who recreational game and that ML methodologies are an effective method of classifying recreational and problematic gamers when using EEG data; (ii) maladaptive coping strategies were significantly associated to gaming scores, and that gamers appeared to experience co-occurrence more so than their non-gamer counterparts; (iii) at-risk and high-risk gamers may utilise gaming as a maladaptive coping strategy and other accompanying potentially addictive behaviour, or substance use may be influenced as a result; (iv) the manifestation of maladaptive coping strategies and potentially addictive behaviours can be influenced by the country in which an individual resides. Taken together, the present doctoral project further clarified the conceptualisation of GD, utilising a neurophysiological underpinning, which is further supported with observed behaviour as suggested by the National Institute of Mental Health. In addition, it places an emphasis on the importance of understanding co-occurrence and specific at-risk factors (e.g., coping) which may contribute to the development and maintenance of problematic or disordered gaming in both a clinical sample and general population samples

Neurophysiology of prospective memory in typical and atypical ageing

The ability to delay an intention is known as ‘prospective memory’ (PM) and underpins many day-to-day activities. The ubiquity of PM makes it essential for independent living in older adults. Research suggests that PM function declines as we age and may be further exacerbated with the development of mild cognitive impairment (MCI). To date, there has been no research examining the neurophysiology of PM in older adults with MCI. This thesis addresses a series of questions to help understand the neurophysiology of PM and how it may be affected by ageing and MCI: 1) Are there neurophysiological differences between highly salient PM cues and less salient PM cues? 2) Can the neurophysiological reorientation of attention be identified in PM tasks? 3) Are there behavioural and neurophysiological differences between young adults, older adults and older adults with MCI during PM tasks? 4) Are there behavioural and neurophysiological differences when maintaining a PM intention between young adults, older adults and older adults with MCI? 5) Can machine learning be used to understand spatiotemporal patterns of brain activity in response to PM between young adults, older adults and older adults with MCI? To answer these questions behavioural and time-locked electroencephalographic (EEG) responses were examined during PM tasks and were modelled with a machine learning method known as Spiking Neural Networks (SNN). Results suggest that: there are behavioural and neurophysiological differences between the PM cues and the neurophysiological reorientation of attention can be detected in PM tasks; older adults are not impaired in PM tasks possibly due to compensatory neural mechanisms; older adults with MCI may be impaired in some PM tasks, which may be due to deficits in attention and feelings of knowing; modelling PM with SNNs may offer useful ways of understanding spatiotemporal connectivity in PM and MCI